149 Climate OSSE with Full-treatment of Sub-grid Cloud Variability and its Application in Longwave Spectral Fingerprinting Studies

Monday, 7 July 2014
Xiuhong Chen, University of Michigan, Ann Arbor, MI; and X. Huang and X. Liu

In order to monitor and attribute secular changes from outgoing spectral radiances, spectral fingerprints need to be constructed first by using an OSSE (Observing System Simulation Experiment). Large-scale model outputs are usually used to derive such spectral fingerprints. Different models make different assumptions on vertical overlapping of sub-grid clouds. We explore the extent to which the spectral fingerprints constructed under different cloud vertical overlapping assumptions can affect such spectral fingerprinting studies. Utilizing PCRTM, a principal component based radiative transfer model with high computational efficiency, we build an OSSE with full treatment of sub-grid cloud variability to study this issue. We first show that the OLR (outgoing longwave radiation) computed from this OSSE is consistent with the OLR directly output from the parent large-scale models, such as ECMWF ERA-Interim and GFDL AM2. We then examine the differences in spectral fingerprints due to three cloud overlapping assumptions alone, i.e. random overlap (RO), maximum-random overlap (MRO), and exponential-random overlap (ERO). Different cloud overlapping assumptions have little effect on the spectral fingerprints of temperature and humidity. However, the amplitude of the spectral fingerprints due to the same amount of cloud fraction change can differ as much as a factor of two between MRO and RO assumptions, especially for middle and low clouds. The differences are most prominent over the mid-IR and near-IR window regions. The results from ERO generally lie between those from MRO and RO. We further examine the impact of cloud overlapping assumptions on the results of linear regression of spectral differences with respect to predefined spectral fingerprints. Cloud-relevant regression coefficients are affected more by different cloud overlapping assumptions than regression coefficients of other geophysical variables. These findings highlight the challenges in constructing realistic longwave spectral fingerprints and in detecting climate change using all-sky observations.

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